Overview

Dataset statistics

Number of variables25
Number of observations21347
Missing cells105
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory288.6 B

Variable types

Categorical3
Numeric22

Alerts

CO is highly overall correlated with SO2 and 17 other fieldsHigh correlation
O3 is highly overall correlated with O3_8hr_max and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 17 other fieldsHigh correlation
PM25 is highly overall correlated with CO and 17 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 17 other fieldsHigh correlation
NH3 is highly overall correlated with CO and 17 other fieldsHigh correlation
NOx is highly overall correlated with CO and 17 other fieldsHigh correlation
PM10_24hr_avg is highly overall correlated with CO and 18 other fieldsHigh correlation
PM25_24hr_avg is highly overall correlated with CO and 18 other fieldsHigh correlation
SO2_24hr_avg is highly overall correlated with CO and 17 other fieldsHigh correlation
NOx_24hr_avg is highly overall correlated with CO and 17 other fieldsHigh correlation
NH3_24hr_avg is highly overall correlated with CO and 17 other fieldsHigh correlation
CO_8hr_max is highly overall correlated with CO and 17 other fieldsHigh correlation
O3_8hr_max is highly overall correlated with O3 and 2 other fieldsHigh correlation
PM25_SubIndex is highly overall correlated with CO and 19 other fieldsHigh correlation
PM10_SubIndex is highly overall correlated with CO and 19 other fieldsHigh correlation
SO2_SubIndex is highly overall correlated with CO and 17 other fieldsHigh correlation
NOx_SubIndex is highly overall correlated with CO and 17 other fieldsHigh correlation
NH3_SubIndex is highly overall correlated with CO and 17 other fieldsHigh correlation
CO_SubIndex is highly overall correlated with CO and 17 other fieldsHigh correlation
O3_SubIndex is highly overall correlated with O3 and 2 other fieldsHigh correlation
AQI_calculated is highly overall correlated with CO and 20 other fieldsHigh correlation
AQIclass is highly overall correlated with PM25_SubIndex and 1 other fieldsHigh correlation
AQI_bucket_calculated is highly overall correlated with PM10_24hr_avg and 4 other fieldsHigh correlation
Checks is highly imbalanced (96.2%)Imbalance
O3 has 749 (3.5%) zerosZeros

Reproduction

Analysis started2023-06-20 05:16:49.597997
Analysis finished2023-06-20 05:19:28.538926
Duration2 minutes and 38.94 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

AQIclass
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
5
10036 
1
4742 
4
3223 
2
1834 
3
1512 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21347
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 10036
47.0%
1 4742
22.2%
4 3223
 
15.1%
2 1834
 
8.6%
3 1512
 
7.1%

Length

2023-06-20T05:19:28.689805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T05:19:28.970965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5 10036
47.0%
1 4742
22.2%
4 3223
 
15.1%
2 1834
 
8.6%
3 1512
 
7.1%

Most occurring characters

ValueCountFrequency (%)
5 10036
47.0%
1 4742
22.2%
4 3223
 
15.1%
2 1834
 
8.6%
3 1512
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21347
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10036
47.0%
1 4742
22.2%
4 3223
 
15.1%
2 1834
 
8.6%
3 1512
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10036
47.0%
1 4742
22.2%
4 3223
 
15.1%
2 1834
 
8.6%
3 1512
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10036
47.0%
1 4742
22.2%
4 3223
 
15.1%
2 1834
 
8.6%
3 1512
 
7.1%

CO
Real number (ℝ)

Distinct369
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78027616
Minimum0.18024
Maximum5.92804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:29.236324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.18024
5-th percentile0.21362
Q10.28038
median0.50736
Q31.00136
95-th percentile2.34985
Maximum5.92804
Range5.7478
Interquartile range (IQR)0.72098

Descriptive statistics

Standard deviation0.71358049
Coefficient of variation (CV)0.91452299
Kurtosis4.7741686
Mean0.78027616
Median Absolute Deviation (MAD)0.26036
Skewness2.030239
Sum16656.555
Variance0.50919712
MonotonicityNot monotonic
2023-06-20T05:19:29.531769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23365 267
 
1.3%
0.24033 267
 
1.3%
0.23699 266
 
1.2%
0.247 259
 
1.2%
0.2203 252
 
1.2%
0.22697 251
 
1.2%
0.24366 251
 
1.2%
0.21696 244
 
1.1%
0.23031 244
 
1.1%
0.25034 225
 
1.1%
Other values (359) 18821
88.2%
ValueCountFrequency (%)
0.18024 2
 
< 0.1%
0.18191 9
 
< 0.1%
0.18358 7
 
< 0.1%
0.18525 10
 
< 0.1%
0.18692 15
0.1%
0.18859 14
0.1%
0.19026 12
0.1%
0.19193 18
0.1%
0.1936 26
0.1%
0.19526 12
0.1%
ValueCountFrequency (%)
5.92804 1
< 0.1%
5.82123 1
< 0.1%
5.6076 2
< 0.1%
5.50079 1
< 0.1%
5.44739 1
< 0.1%
5.39398 1
< 0.1%
5.28717 1
< 0.1%
5.07355 2
< 0.1%
5.02014 2
< 0.1%
4.91333 2
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct696
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.371319
Minimum0
Maximum360.49
Zeros749
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:29.842328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q113.95
median30.76
Q377.96
95-th percentile164.081
Maximum360.49
Range360.49
Interquartile range (IQR)64.01

Descriptive statistics

Standard deviation54.348564
Coefficient of variation (CV)1.0377544
Kurtosis1.1326758
Mean52.371319
Median Absolute Deviation (MAD)24.41
Skewness1.3222051
Sum1117970.6
Variance2953.7664
MonotonicityNot monotonic
2023-06-20T05:19:30.127703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 749
 
3.5%
0.01 216
 
1.0%
24.68 143
 
0.7%
25.03 138
 
0.6%
26.11 136
 
0.6%
25.39 134
 
0.6%
23.25 129
 
0.6%
26.46 119
 
0.6%
25.75 118
 
0.6%
23.6 115
 
0.5%
Other values (686) 19350
90.6%
ValueCountFrequency (%)
0 749
3.5%
0.01 216
 
1.0%
0.02 101
 
0.5%
0.03 73
 
0.3%
0.04 67
 
0.3%
0.05 53
 
0.2%
0.06 46
 
0.2%
0.07 33
 
0.2%
0.08 33
 
0.2%
0.09 27
 
0.1%
ValueCountFrequency (%)
360.49 1
< 0.1%
357.63 1
< 0.1%
340.46 1
< 0.1%
317.57 1
< 0.1%
311.85 2
< 0.1%
308.99 1
< 0.1%
306.13 2
< 0.1%
300.41 2
< 0.1%
294.69 1
< 0.1%
291.82 1
< 0.1%

SO2
Real number (ℝ)

Distinct488
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.63164
Minimum0.98
Maximum146.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:30.810731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile2.77
Q16.26
median11.68
Q320.27
95-th percentile42.44
Maximum146.87
Range145.89
Interquartile range (IQR)14.01

Descriptive statistics

Standard deviation13.58037
Coefficient of variation (CV)0.86877448
Kurtosis7.2089664
Mean15.63164
Median Absolute Deviation (MAD)6.38
Skewness2.1821186
Sum333688.62
Variance184.42645
MonotonicityNot monotonic
2023-06-20T05:19:32.392685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.97 150
 
0.7%
16.45 149
 
0.7%
15.5 148
 
0.7%
16.69 148
 
0.7%
7.75 138
 
0.6%
17.64 137
 
0.6%
9.18 134
 
0.6%
7.99 132
 
0.6%
8.11 132
 
0.6%
16.93 131
 
0.6%
Other values (478) 19948
93.4%
ValueCountFrequency (%)
0.98 1
 
< 0.1%
1.03 1
 
< 0.1%
1.04 3
< 0.1%
1.06 1
 
< 0.1%
1.07 2
< 0.1%
1.1 1
 
< 0.1%
1.13 2
< 0.1%
1.16 1
 
< 0.1%
1.18 2
< 0.1%
1.19 2
< 0.1%
ValueCountFrequency (%)
146.87 1
< 0.1%
135.42 1
< 0.1%
127.79 1
< 0.1%
125.89 1
< 0.1%
123.98 2
< 0.1%
117.3 1
< 0.1%
113.49 1
< 0.1%
108.72 1
< 0.1%
106.81 1
< 0.1%
105.86 2
< 0.1%

PM25
Real number (ℝ)

Distinct11391
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.072552
Minimum0.95
Maximum670.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:33.041866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile4.33
Q111.135
median49.51
Q397.56
95-th percentile205.089
Maximum670.22
Range669.27
Interquartile range (IQR)86.425

Descriptive statistics

Standard deviation70.411765
Coefficient of variation (CV)1.0343635
Kurtosis5.8802648
Mean68.072552
Median Absolute Deviation (MAD)40.25
Skewness1.9530615
Sum1453144.8
Variance4957.8166
MonotonicityNot monotonic
2023-06-20T05:19:33.328252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.95 19
 
0.1%
4.88 17
 
0.1%
6.03 17
 
0.1%
6.15 16
 
0.1%
5.22 16
 
0.1%
5.25 16
 
0.1%
5.7 15
 
0.1%
6.78 15
 
0.1%
5.42 15
 
0.1%
5.32 14
 
0.1%
Other values (11381) 21187
99.3%
ValueCountFrequency (%)
0.95 1
< 0.1%
1.03 1
< 0.1%
1.11 1
< 0.1%
1.27 1
< 0.1%
1.28 1
< 0.1%
1.33 1
< 0.1%
1.36 1
< 0.1%
1.37 1
< 0.1%
1.38 1
< 0.1%
1.39 1
< 0.1%
ValueCountFrequency (%)
670.22 1
< 0.1%
601.3 1
< 0.1%
596.72 1
< 0.1%
596.19 1
< 0.1%
581.06 1
< 0.1%
578.25 1
< 0.1%
578.23 1
< 0.1%
562.33 1
< 0.1%
558.48 1
< 0.1%
556.8 1
< 0.1%

PM10
Real number (ℝ)

Distinct12668
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.65899
Minimum1.59
Maximum727.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:33.639169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.59
5-th percentile5.72
Q116.625
median68.35
Q3125.27
95-th percentile252.787
Maximum727.74
Range726.15
Interquartile range (IQR)108.645

Descriptive statistics

Standard deviation84.302597
Coefficient of variation (CV)0.9617108
Kurtosis4.0216402
Mean87.65899
Median Absolute Deviation (MAD)53.5
Skewness1.6510235
Sum1871256.5
Variance7106.9279
MonotonicityNot monotonic
2023-06-20T05:19:33.924197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.28 14
 
0.1%
6.5 14
 
0.1%
8.11 14
 
0.1%
6.65 13
 
0.1%
7.28 13
 
0.1%
6.9 13
 
0.1%
7.38 13
 
0.1%
5.73 12
 
0.1%
7.66 12
 
0.1%
6.96 12
 
0.1%
Other values (12658) 21217
99.4%
ValueCountFrequency (%)
1.59 1
< 0.1%
1.74 1
< 0.1%
1.79 1
< 0.1%
1.82 1
< 0.1%
1.84 1
< 0.1%
1.85 1
< 0.1%
1.86 1
< 0.1%
1.87 1
< 0.1%
1.9 1
< 0.1%
1.96 1
< 0.1%
ValueCountFrequency (%)
727.74 1
< 0.1%
675.25 1
< 0.1%
647.98 1
< 0.1%
644.28 1
< 0.1%
642.57 1
< 0.1%
642.21 1
< 0.1%
635.88 1
< 0.1%
633.06 1
< 0.1%
608.44 1
< 0.1%
607.56 1
< 0.1%

NH3
Real number (ℝ)

Distinct571
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.469802
Minimum0
Maximum170.23
Zeros19
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:34.256027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.88
Q11.81
median4.75
Q314.44
95-th percentile44.58
Maximum170.23
Range170.23
Interquartile range (IQR)12.63

Descriptive statistics

Standard deviation16.875436
Coefficient of variation (CV)1.4712926
Kurtosis12.9635
Mean11.469802
Median Absolute Deviation (MAD)3.55
Skewness3.1585897
Sum244845.87
Variance284.78033
MonotonicityNot monotonic
2023-06-20T05:19:34.643862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28 101
 
0.5%
2.09 99
 
0.5%
2.15 97
 
0.5%
1.11 96
 
0.4%
1.12 95
 
0.4%
2.06 94
 
0.4%
2.22 93
 
0.4%
2.31 92
 
0.4%
1.01 89
 
0.4%
2.41 89
 
0.4%
Other values (561) 20402
95.6%
ValueCountFrequency (%)
0 19
0.1%
0.01 1
 
< 0.1%
0.02 2
 
< 0.1%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.05 3
 
< 0.1%
0.06 1
 
< 0.1%
0.07 5
 
< 0.1%
0.1 2
 
< 0.1%
0.11 2
 
< 0.1%
ValueCountFrequency (%)
170.23 1
 
< 0.1%
166.18 1
 
< 0.1%
162.12 1
 
< 0.1%
160.1 1
 
< 0.1%
158.07 1
 
< 0.1%
145.91 3
< 0.1%
141.86 1
 
< 0.1%
139.83 3
< 0.1%
135.78 6
< 0.1%
133.75 3
< 0.1%

NOx
Real number (ℝ)

Distinct7948
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.816367
Minimum0.91
Maximum272.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:35.115520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.91
5-th percentile2.82
Q16.24
median14.27
Q334.695
95-th percentile96.227
Maximum272.93
Range272.02
Interquartile range (IQR)28.455

Descriptive statistics

Standard deviation31.755658
Coefficient of variation (CV)1.1841894
Kurtosis6.9682673
Mean26.816367
Median Absolute Deviation (MAD)9.8
Skewness2.363714
Sum572448.99
Variance1008.4218
MonotonicityNot monotonic
2023-06-20T05:19:35.658854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.28 42
 
0.2%
13.54 36
 
0.2%
13.02 36
 
0.2%
22.96 35
 
0.2%
12 35
 
0.2%
16.28 33
 
0.2%
10.97 33
 
0.2%
15.08 33
 
0.2%
12.17 32
 
0.1%
16.45 31
 
0.1%
Other values (7938) 21001
98.4%
ValueCountFrequency (%)
0.91 2
< 0.1%
0.93 1
< 0.1%
0.95 2
< 0.1%
0.95 1
< 0.1%
0.96 1
< 0.1%
0.98 1
< 0.1%
0.99 2
< 0.1%
1 1
< 0.1%
1.01 1
< 0.1%
1.04 1
< 0.1%
ValueCountFrequency (%)
272.93 1
< 0.1%
270.48 1
< 0.1%
269.52 1
< 0.1%
256.06 1
< 0.1%
254.95 1
< 0.1%
252.79 1
< 0.1%
247.18 1
< 0.1%
247.15 1
< 0.1%
245.33 1
< 0.1%
242.11 1
< 0.1%

PM10_24hr_avg
Real number (ℝ)

Distinct20792
Distinct (%)97.5%
Missing15
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean87.697166
Minimum2.7841667
Maximum379.49958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:36.146071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.7841667
5-th percentile6.3873125
Q118.559687
median82.486667
Q3127.74458
95-th percentile217.8349
Maximum379.49958
Range376.71542
Interquartile range (IQR)109.1849

Descriptive statistics

Standard deviation70.733756
Coefficient of variation (CV)0.80656833
Kurtosis0.80838033
Mean87.697166
Median Absolute Deviation (MAD)53.56
Skewness0.91162982
Sum1870755.9
Variance5003.2642
MonotonicityNot monotonic
2023-06-20T05:19:36.684835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.789166667 3
 
< 0.1%
9.314583333 3
 
< 0.1%
11.00291667 3
 
< 0.1%
10.25583333 3
 
< 0.1%
6.694583333 3
 
< 0.1%
85.85833333 3
 
< 0.1%
7.44625 3
 
< 0.1%
6.855833333 3
 
< 0.1%
7.470416667 3
 
< 0.1%
7.00625 3
 
< 0.1%
Other values (20782) 21302
99.8%
(Missing) 15
 
0.1%
ValueCountFrequency (%)
2.784166667 1
< 0.1%
2.793333333 1
< 0.1%
2.795416667 1
< 0.1%
2.820833333 1
< 0.1%
2.826666667 1
< 0.1%
2.85125 1
< 0.1%
2.860833333 1
< 0.1%
2.8625 1
< 0.1%
2.883333333 1
< 0.1%
2.890416667 1
< 0.1%
ValueCountFrequency (%)
379.4995833 1
< 0.1%
378.7533333 1
< 0.1%
375.3783333 1
< 0.1%
375.275 1
< 0.1%
375.2341667 1
< 0.1%
373.2829167 1
< 0.1%
373.2275 1
< 0.1%
372.0641667 1
< 0.1%
371.9858333 1
< 0.1%
371.2391667 1
< 0.1%

PM25_24hr_avg
Real number (ℝ)

Distinct20571
Distinct (%)96.4%
Missing15
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean68.101566
Minimum1.7875
Maximum339.7475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:37.164661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.7875
5-th percentile4.8948125
Q112.303125
median59.632083
Q3100.97906
95-th percentile183.09721
Maximum339.7475
Range337.96
Interquartile range (IQR)88.675938

Descriptive statistics

Standard deviation59.483897
Coefficient of variation (CV)0.87345858
Kurtosis1.5368593
Mean68.101566
Median Absolute Deviation (MAD)44.863333
Skewness1.164851
Sum1452742.6
Variance3538.334
MonotonicityNot monotonic
2023-06-20T05:19:37.656125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.92875 5
 
< 0.1%
4.927083333 4
 
< 0.1%
5.169166667 3
 
< 0.1%
5.880416667 3
 
< 0.1%
5.359583333 3
 
< 0.1%
5.977916667 3
 
< 0.1%
4.919166667 3
 
< 0.1%
5.472916667 3
 
< 0.1%
5.8225 3
 
< 0.1%
8.216666667 3
 
< 0.1%
Other values (20561) 21299
99.8%
(Missing) 15
 
0.1%
ValueCountFrequency (%)
1.7875 1
< 0.1%
1.795416667 1
< 0.1%
1.7975 1
< 0.1%
1.817083333 1
< 0.1%
1.825416667 1
< 0.1%
1.844166667 1
< 0.1%
1.87375 1
< 0.1%
1.875833333 1
< 0.1%
1.87875 1
< 0.1%
1.88625 1
< 0.1%
ValueCountFrequency (%)
339.7475 1
< 0.1%
339.1866667 1
< 0.1%
339.0966667 1
< 0.1%
338.195 1
< 0.1%
337.11875 1
< 0.1%
336.61375 1
< 0.1%
335.9529167 1
< 0.1%
334.2675 1
< 0.1%
334.10625 1
< 0.1%
333.73125 1
< 0.1%

SO2_24hr_avg
Real number (ℝ)

Distinct18511
Distinct (%)86.8%
Missing15
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean15.638105
Minimum1.45375
Maximum56.693333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:38.201222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.45375
5-th percentile3.2205
Q17.0619792
median14.8325
Q321.014792
95-th percentile34.326583
Maximum56.693333
Range55.239583
Interquartile range (IQR)13.952813

Descriptive statistics

Standard deviation9.7909269
Coefficient of variation (CV)0.62609419
Kurtosis0.61475844
Mean15.638105
Median Absolute Deviation (MAD)7.1239583
Skewness0.85008109
Sum333592.06
Variance95.862249
MonotonicityNot monotonic
2023-06-20T05:19:38.750516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.66666667 7
 
< 0.1%
16.99208333 5
 
< 0.1%
20.06708333 5
 
< 0.1%
3.628333333 4
 
< 0.1%
14.26041667 4
 
< 0.1%
17.3 4
 
< 0.1%
16.79375 4
 
< 0.1%
5.172916667 4
 
< 0.1%
19.585 4
 
< 0.1%
16.8875 4
 
< 0.1%
Other values (18501) 21287
99.7%
(Missing) 15
 
0.1%
ValueCountFrequency (%)
1.45375 2
< 0.1%
1.455 1
< 0.1%
1.455833333 1
< 0.1%
1.4575 1
< 0.1%
1.45875 1
< 0.1%
1.46 1
< 0.1%
1.46375 1
< 0.1%
1.465833333 1
< 0.1%
1.469166667 1
< 0.1%
1.473333333 1
< 0.1%
ValueCountFrequency (%)
56.69333333 1
< 0.1%
56.21666667 1
< 0.1%
56.19666667 1
< 0.1%
55.38208333 1
< 0.1%
54.86541667 1
< 0.1%
54.38875 1
< 0.1%
53.89208333 1
< 0.1%
53.68291667 1
< 0.1%
53.64375 1
< 0.1%
53.29541667 1
< 0.1%

NOx_24hr_avg
Real number (ℝ)

Distinct20036
Distinct (%)93.9%
Missing15
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean26.827955
Minimum1.85625
Maximum125.06625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:39.201614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.85625
5-th percentile4.0506875
Q18.7576042
median21.359583
Q338.719167
95-th percentile71.228292
Maximum125.06625
Range123.21
Interquartile range (IQR)29.961562

Descriptive statistics

Standard deviation21.543442
Coefficient of variation (CV)0.80302213
Kurtosis1.406553
Mean26.827955
Median Absolute Deviation (MAD)13.571875
Skewness1.2354414
Sum572293.94
Variance464.11989
MonotonicityNot monotonic
2023-06-20T05:19:39.493786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.93625 4
 
< 0.1%
9.610833333 4
 
< 0.1%
4.03375 4
 
< 0.1%
18.08208333 4
 
< 0.1%
5.323333333 4
 
< 0.1%
6.272916667 3
 
< 0.1%
6.51625 3
 
< 0.1%
3.672916667 3
 
< 0.1%
3.561666667 3
 
< 0.1%
3.507916667 3
 
< 0.1%
Other values (20026) 21297
99.8%
(Missing) 15
 
0.1%
ValueCountFrequency (%)
1.85625 1
< 0.1%
1.857083333 1
< 0.1%
1.859583333 1
< 0.1%
1.859583333 1
< 0.1%
1.869166667 1
< 0.1%
1.870416667 1
< 0.1%
1.8825 1
< 0.1%
1.892916667 1
< 0.1%
1.893333333 1
< 0.1%
1.900416667 1
< 0.1%
ValueCountFrequency (%)
125.06625 1
< 0.1%
124.9816667 1
< 0.1%
124.2641667 1
< 0.1%
124.2379167 1
< 0.1%
123.1408333 1
< 0.1%
122.8025 1
< 0.1%
122.1491667 1
< 0.1%
122.14125 1
< 0.1%
122.1208333 1
< 0.1%
122.0483333 1
< 0.1%

NH3_24hr_avg
Real number (ℝ)

Distinct17455
Distinct (%)81.8%
Missing15
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.475019
Minimum0.6875
Maximum69.420417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:39.799449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.6875
5-th percentile1.0698125
Q12.0002083
median7.40375
Q317.067396
95-th percentile35.515167
Maximum69.420417
Range68.732917
Interquartile range (IQR)15.067187

Descriptive statistics

Standard deviation11.651044
Coefficient of variation (CV)1.0153398
Kurtosis2.0370214
Mean11.475019
Median Absolute Deviation (MAD)5.8697917
Skewness1.4503845
Sum244785.11
Variance135.74682
MonotonicityNot monotonic
2023-06-20T05:19:40.097852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.26875 26
 
0.1%
19.91125 25
 
0.1%
0.9516666667 8
 
< 0.1%
1.627916667 7
 
< 0.1%
0.9691666667 7
 
< 0.1%
1.61875 7
 
< 0.1%
1.650416667 7
 
< 0.1%
0.9383333333 7
 
< 0.1%
1.3825 7
 
< 0.1%
0.9670833333 7
 
< 0.1%
Other values (17445) 21224
99.4%
(Missing) 15
 
0.1%
ValueCountFrequency (%)
0.6875 1
< 0.1%
0.6879166667 1
< 0.1%
0.6891666667 1
< 0.1%
0.6916666667 1
< 0.1%
0.6920833333 1
< 0.1%
0.6925 1
< 0.1%
0.6929166667 1
< 0.1%
0.69375 1
< 0.1%
0.695 1
< 0.1%
0.6958333333 1
< 0.1%
ValueCountFrequency (%)
69.42041667 1
< 0.1%
69.29375 1
< 0.1%
68.99833333 1
< 0.1%
68.91375 1
< 0.1%
68.51291667 1
< 0.1%
68.48375 1
< 0.1%
68.42541667 1
< 0.1%
68.30166667 1
< 0.1%
68.175 1
< 0.1%
68.15375 1
< 0.1%

CO_8hr_max
Real number (ℝ)

Distinct334
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1676227
Minimum0.19527
Maximum5.92804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:40.416385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.19527
5-th percentile0.24033
Q10.33712
median0.80776
Q31.73569
95-th percentile3.15094
Maximum5.92804
Range5.73277
Interquartile range (IQR)1.39857

Descriptive statistics

Standard deviation0.99477868
Coefficient of variation (CV)0.8519693
Kurtosis1.0617854
Mean1.1676227
Median Absolute Deviation (MAD)0.52738
Skewness1.2291316
Sum24925.242
Variance0.98958463
MonotonicityNot monotonic
2023-06-20T05:19:40.706041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26035 249
 
1.2%
0.28706 207
 
1.0%
0.25034 207
 
1.0%
0.28372 202
 
0.9%
0.24033 199
 
0.9%
0.25368 196
 
0.9%
0.27704 192
 
0.9%
0.25702 189
 
0.9%
0.247 188
 
0.9%
0.26369 187
 
0.9%
Other values (324) 19331
90.6%
ValueCountFrequency (%)
0.19527 1
 
< 0.1%
0.19693 9
 
< 0.1%
0.1986 3
 
< 0.1%
0.20027 1
 
< 0.1%
0.20194 3
 
< 0.1%
0.20361 4
 
< 0.1%
0.20528 6
 
< 0.1%
0.20695 7
 
< 0.1%
0.20862 19
0.1%
0.21029 33
0.2%
ValueCountFrequency (%)
5.92804 8
< 0.1%
5.82123 8
< 0.1%
5.6076 9
< 0.1%
5.50079 8
< 0.1%
5.44739 1
 
< 0.1%
5.39398 1
 
< 0.1%
5.28717 1
 
< 0.1%
5.07355 16
0.1%
5.02014 2
 
< 0.1%
4.91333 2
 
< 0.1%

O3_8hr_max
Real number (ℝ)

Distinct636
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.745381
Minimum0
Maximum360.49
Zeros123
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:40.990907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.53
Q129.33
median65.09
Q3140.19
95-th percentile211.72
Maximum360.49
Range360.49
Interquartile range (IQR)110.86

Descriptive statistics

Standard deviation67.953649
Coefficient of variation (CV)0.78336908
Kurtosis-0.25467589
Mean86.745381
Median Absolute Deviation (MAD)43.81
Skewness0.75580395
Sum1851753.6
Variance4617.6984
MonotonicityNot monotonic
2023-06-20T05:19:41.269732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147.34 182
 
0.9%
150.2 172
 
0.8%
164.51 155
 
0.7%
131.61 153
 
0.7%
185.97 145
 
0.7%
158.79 143
 
0.7%
135.9 139
 
0.7%
26.46 138
 
0.6%
26.11 137
 
0.6%
134.47 133
 
0.6%
Other values (626) 19850
93.0%
ValueCountFrequency (%)
0 123
0.6%
0.01 54
0.3%
0.02 34
 
0.2%
0.03 19
 
0.1%
0.04 22
 
0.1%
0.05 21
 
0.1%
0.06 10
 
< 0.1%
0.07 13
 
0.1%
0.08 2
 
< 0.1%
0.09 8
 
< 0.1%
ValueCountFrequency (%)
360.49 8
< 0.1%
357.63 8
< 0.1%
340.46 8
< 0.1%
317.57 1
 
< 0.1%
311.85 16
0.1%
308.99 8
< 0.1%
306.13 16
0.1%
300.41 9
< 0.1%
294.69 8
< 0.1%
291.82 8
< 0.1%

PM25_SubIndex
Real number (ℝ)

Distinct20546
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.80637
Minimum0
Maximum469.03654
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:41.555482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.1364583
Q120.3625
median99.193056
Q3236.49986
95-th percentile348.49353
Maximum469.03654
Range469.03654
Interquartile range (IQR)216.13736

Descriptive statistics

Standard deviation120.12457
Coefficient of variation (CV)0.87806273
Kurtosis-0.8800246
Mean136.80637
Median Absolute Deviation (MAD)85.931944
Skewness0.63369448
Sum2920405.6
Variance14429.913
MonotonicityNot monotonic
2023-06-20T05:19:41.863985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
0.1%
8.214583333 5
 
< 0.1%
9.409027778 4
 
< 0.1%
8.211805556 4
 
< 0.1%
13.69444444 3
 
< 0.1%
9.452777778 3
 
< 0.1%
9.497222222 3
 
< 0.1%
15.38055556 3
 
< 0.1%
9.222916667 3
 
< 0.1%
8.999305556 3
 
< 0.1%
Other values (20536) 21301
99.8%
ValueCountFrequency (%)
0 15
0.1%
2.979166667 1
 
< 0.1%
2.992361111 1
 
< 0.1%
2.995833333 1
 
< 0.1%
3.028472222 1
 
< 0.1%
3.042361111 1
 
< 0.1%
3.073611111 1
 
< 0.1%
3.122916667 1
 
< 0.1%
3.126388889 1
 
< 0.1%
3.13125 1
 
< 0.1%
ValueCountFrequency (%)
469.0365385 1
< 0.1%
468.6051282 1
< 0.1%
468.5358974 1
< 0.1%
467.8423077 1
< 0.1%
467.0144231 1
< 0.1%
466.6259615 1
< 0.1%
466.1176282 1
< 0.1%
464.8211538 1
< 0.1%
464.6971154 1
< 0.1%
464.4086538 1
< 0.1%

PM10_SubIndex
Real number (ℝ)

Distinct20785
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.459325
Minimum0
Maximum336.87448
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:42.139437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.3634583
Q118.414583
median82.46625
Q3118.47069
95-th percentile178.52081
Maximum336.87448
Range336.87448
Interquartile range (IQR)100.05611

Descriptive statistics

Standard deviation59.668543
Coefficient of variation (CV)0.74159885
Kurtosis0.40192797
Mean80.459325
Median Absolute Deviation (MAD)45.959306
Skewness0.63237703
Sum1717565.2
Variance3560.335
MonotonicityNot monotonic
2023-06-20T05:19:42.430072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
0.1%
6.63875 3
 
< 0.1%
6.855833333 3
 
< 0.1%
11.00291667 3
 
< 0.1%
85.85833333 3
 
< 0.1%
6.694583333 3
 
< 0.1%
47.24333333 3
 
< 0.1%
9.314583333 3
 
< 0.1%
6.518333333 3
 
< 0.1%
10.25583333 3
 
< 0.1%
Other values (20775) 21305
99.8%
ValueCountFrequency (%)
0 15
0.1%
2.784166667 1
 
< 0.1%
2.793333333 1
 
< 0.1%
2.795416667 1
 
< 0.1%
2.820833333 1
 
< 0.1%
2.826666667 1
 
< 0.1%
2.85125 1
 
< 0.1%
2.860833333 1
 
< 0.1%
2.8625 1
 
< 0.1%
2.883333333 1
 
< 0.1%
ValueCountFrequency (%)
336.8744792 1
< 0.1%
335.9416667 1
< 0.1%
331.7229167 1
< 0.1%
331.59375 1
< 0.1%
331.5427083 1
< 0.1%
329.1036458 1
< 0.1%
329.034375 1
< 0.1%
327.5802083 1
< 0.1%
327.4822917 1
< 0.1%
326.5489583 1
< 0.1%

SO2_SubIndex
Real number (ℝ)

Distinct18342
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.533896
Minimum0
Maximum70.866667
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:42.727260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.0067188
Q18.8111979
median18.527604
Q326.263542
95-th percentile42.899323
Maximum70.866667
Range70.866667
Interquartile range (IQR)17.452344

Descriptive statistics

Standard deviation12.245319
Coefficient of variation (CV)0.62687539
Kurtosis0.6127296
Mean19.533896
Median Absolute Deviation (MAD)8.9140625
Skewness0.84860494
Sum416990.08
Variance149.94783
MonotonicityNot monotonic
2023-06-20T05:19:43.053535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
0.1%
19.58333333 7
 
< 0.1%
25.08385417 5
 
< 0.1%
21.24010417 5
 
< 0.1%
17.82552083 4
 
< 0.1%
4.1671875 4
 
< 0.1%
39.01614583 4
 
< 0.1%
7.252604167 4
 
< 0.1%
6.209895833 4
 
< 0.1%
7.369270833 4
 
< 0.1%
Other values (18332) 21291
99.7%
ValueCountFrequency (%)
0 15
0.1%
1.8171875 2
 
< 0.1%
1.81875 1
 
< 0.1%
1.819791667 1
 
< 0.1%
1.821875 1
 
< 0.1%
1.8234375 1
 
< 0.1%
1.825 1
 
< 0.1%
1.8296875 1
 
< 0.1%
1.832291667 1
 
< 0.1%
1.836458333 1
 
< 0.1%
ValueCountFrequency (%)
70.86666667 1
< 0.1%
70.27083333 1
< 0.1%
70.24583333 1
< 0.1%
69.22760417 1
< 0.1%
68.58177083 1
< 0.1%
67.9859375 1
< 0.1%
67.36510417 1
< 0.1%
67.10364583 1
< 0.1%
67.0546875 1
< 0.1%
66.61927083 1
< 0.1%

NOx_SubIndex
Real number (ℝ)

Distinct19940
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.418089
Minimum0
Maximum145.06625
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:43.356120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.0421875
Q110.934115
median26.664583
Q348.39375
95-th percentile89.000365
Maximum145.06625
Range145.06625
Interquartile range (IQR)37.459635

Descriptive statistics

Standard deviation26.616672
Coefficient of variation (CV)0.79647497
Kurtosis1.0391552
Mean33.418089
Median Absolute Deviation (MAD)16.954687
Skewness1.1645975
Sum713375.95
Variance708.44721
MonotonicityNot monotonic
2023-06-20T05:19:43.636949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
0.1%
5.0421875 4
 
< 0.1%
22.60260417 4
 
< 0.1%
12.01354167 4
 
< 0.1%
6.654166667 4
 
< 0.1%
36.1703125 4
 
< 0.1%
4.736979167 4
 
< 0.1%
4.591145833 3
 
< 0.1%
5.00625 3
 
< 0.1%
3.861458333 3
 
< 0.1%
Other values (19930) 21299
99.8%
ValueCountFrequency (%)
0 15
0.1%
2.3203125 1
 
< 0.1%
2.321354167 1
 
< 0.1%
2.324479167 1
 
< 0.1%
2.324479167 1
 
< 0.1%
2.336458333 1
 
< 0.1%
2.338020833 1
 
< 0.1%
2.353125 1
 
< 0.1%
2.366145833 1
 
< 0.1%
2.366666667 1
 
< 0.1%
ValueCountFrequency (%)
145.06625 1
< 0.1%
144.9816667 1
< 0.1%
144.2641667 1
< 0.1%
144.2379167 1
< 0.1%
143.1408333 1
< 0.1%
142.8025 1
< 0.1%
142.1491667 1
< 0.1%
142.14125 1
< 0.1%
142.1208333 1
< 0.1%
142.0483333 1
< 0.1%

NH3_SubIndex
Real number (ℝ)

Distinct17356
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866739
Minimum0
Maximum17.355104
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:43.945209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26544792
Q10.49921875
median1.8482292
Q34.2647917
95-th percentile8.8770417
Maximum17.355104
Range17.355104
Interquartile range (IQR)3.7655729

Descriptive statistics

Standard deviation2.9127296
Coefficient of variation (CV)1.0160428
Kurtosis2.0383733
Mean2.866739
Median Absolute Deviation (MAD)1.4657292
Skewness1.450817
Sum61196.278
Variance8.4839935
MonotonicityNot monotonic
2023-06-20T05:19:44.227812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5671875 26
 
0.1%
4.9778125 25
 
0.1%
0 15
 
0.1%
0.2379166667 8
 
< 0.1%
0.4046875 7
 
< 0.1%
0.4069791667 7
 
< 0.1%
0.2417708333 7
 
< 0.1%
0.2422916667 7
 
< 0.1%
0.345625 7
 
< 0.1%
0.2345833333 7
 
< 0.1%
Other values (17346) 21231
99.5%
ValueCountFrequency (%)
0 15
0.1%
0.171875 1
 
< 0.1%
0.1719791667 1
 
< 0.1%
0.1722916667 1
 
< 0.1%
0.1729166667 1
 
< 0.1%
0.1730208333 1
 
< 0.1%
0.173125 1
 
< 0.1%
0.1732291667 1
 
< 0.1%
0.1734375 1
 
< 0.1%
0.17375 1
 
< 0.1%
ValueCountFrequency (%)
17.35510417 1
< 0.1%
17.3234375 1
< 0.1%
17.24958333 1
< 0.1%
17.2284375 1
< 0.1%
17.12822917 1
< 0.1%
17.1209375 1
< 0.1%
17.10635417 1
< 0.1%
17.07541667 1
< 0.1%
17.04375 1
< 0.1%
17.0384375 1
< 0.1%

CO_SubIndex
Real number (ℝ)

Distinct334
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.155535
Minimum9.7635
Maximum149.1005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:44.536221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.7635
5-th percentile12.0165
Q116.856
median40.388
Q386.7845
95-th percentile114.38675
Maximum149.1005
Range139.337
Interquartile range (IQR)69.9285

Descriptive statistics

Standard deviation37.233938
Coefficient of variation (CV)0.71390195
Kurtosis-1.1706199
Mean52.155535
Median Absolute Deviation (MAD)26.369
Skewness0.53558275
Sum1113364.2
Variance1386.3661
MonotonicityNot monotonic
2023-06-20T05:19:44.809005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.0175 249
 
1.2%
14.353 207
 
1.0%
12.517 207
 
1.0%
14.186 202
 
0.9%
12.0165 199
 
0.9%
12.684 196
 
0.9%
13.852 192
 
0.9%
12.851 189
 
0.9%
12.35 188
 
0.9%
13.1845 187
 
0.9%
Other values (324) 19331
90.6%
ValueCountFrequency (%)
9.7635 1
 
< 0.1%
9.8465 9
 
< 0.1%
9.93 3
 
< 0.1%
10.0135 1
 
< 0.1%
10.097 3
 
< 0.1%
10.1805 4
 
< 0.1%
10.264 6
 
< 0.1%
10.3475 7
 
< 0.1%
10.431 19
0.1%
10.5145 33
0.2%
ValueCountFrequency (%)
149.1005 8
< 0.1%
147.765375 8
< 0.1%
145.095 9
< 0.1%
143.759875 8
< 0.1%
143.092375 1
 
< 0.1%
142.42475 1
 
< 0.1%
141.089625 1
 
< 0.1%
138.419375 16
0.1%
137.75175 2
 
< 0.1%
136.416625 2
 
< 0.1%

O3_SubIndex
Real number (ℝ)

Distinct636
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.458959
Minimum0
Maximum328.29128
Zeros123
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:45.125418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.53
Q129.33
median65.09
Q3159.10294
95-th percentile300.69017
Maximum328.29128
Range328.29128
Interquartile range (IQR)129.77294

Descriptive statistics

Standard deviation87.435687
Coefficient of variation (CV)0.87911324
Kurtosis-0.16820861
Mean99.458959
Median Absolute Deviation (MAD)44.35
Skewness0.95129135
Sum2123150.4
Variance7644.9994
MonotonicityNot monotonic
2023-06-20T05:19:45.406908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.6176471 182
 
0.9%
173.8235294 172
 
0.8%
194.8676471 155
 
0.7%
146.4852941 153
 
0.7%
244.925 145
 
0.7%
186.4558824 143
 
0.7%
152.7941176 139
 
0.7%
26.46 138
 
0.6%
26.11 137
 
0.6%
150.6911765 133
 
0.6%
Other values (626) 19850
93.0%
ValueCountFrequency (%)
0 123
0.6%
0.01 54
0.3%
0.02 34
 
0.2%
0.03 19
 
0.1%
0.04 22
 
0.1%
0.05 21
 
0.1%
0.06 10
 
< 0.1%
0.07 13
 
0.1%
0.08 2
 
< 0.1%
0.09 8
 
< 0.1%
ValueCountFrequency (%)
328.2912801 8
< 0.1%
327.7606679 8
< 0.1%
324.5751391 8
< 0.1%
320.3283859 1
 
< 0.1%
319.2671614 16
0.1%
318.7365492 8
< 0.1%
318.2059369 16
0.1%
317.1447124 9
< 0.1%
316.0834879 8
< 0.1%
315.5510204 8
< 0.1%

Checks
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
7
21209 
6
 
123
2
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21347
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
7 21209
99.4%
6 123
 
0.6%
2 15
 
0.1%

Length

2023-06-20T05:19:45.680853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T05:19:45.939728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
7 21209
99.4%
6 123
 
0.6%
2 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
7 21209
99.4%
6 123
 
0.6%
2 15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21347
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 21209
99.4%
6 123
 
0.6%
2 15
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 21209
99.4%
6 123
 
0.6%
2 15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 21209
99.4%
6 123
 
0.6%
2 15
 
0.1%

AQI_calculated
Real number (ℝ)

Distinct453
Distinct (%)2.1%
Missing15
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean163.59366
Minimum14
Maximum469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size333.5 KiB
2023-06-20T05:19:46.182626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile23
Q145
median149
Q3268
95-th percentile348.45
Maximum469
Range455
Interquartile range (IQR)223

Descriptive statistics

Standard deviation115.67952
Coefficient of variation (CV)0.70711494
Kurtosis-1.1677657
Mean163.59366
Median Absolute Deviation (MAD)108
Skewness0.34799406
Sum3489780
Variance13381.752
MonotonicityNot monotonic
2023-06-20T05:19:46.459537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 326
 
1.5%
25 321
 
1.5%
28 272
 
1.3%
33 271
 
1.3%
34 252
 
1.2%
29 251
 
1.2%
23 251
 
1.2%
24 233
 
1.1%
35 219
 
1.0%
21 212
 
1.0%
Other values (443) 18724
87.7%
ValueCountFrequency (%)
14 12
 
0.1%
15 14
 
0.1%
16 65
 
0.3%
17 84
 
0.4%
18 82
 
0.4%
19 128
0.6%
20 139
0.7%
21 212
1.0%
22 197
0.9%
23 251
1.2%
ValueCountFrequency (%)
469 3
< 0.1%
468 1
 
< 0.1%
467 2
< 0.1%
466 1
 
< 0.1%
465 2
< 0.1%
464 2
< 0.1%
463 1
 
< 0.1%
462 4
< 0.1%
460 3
< 0.1%
458 3
< 0.1%
Distinct6
Distinct (%)< 0.1%
Missing15
Missing (%)0.1%
Memory size1.5 MiB
Good
5672 
Moderate
5033 
Very Poor
4029 
Poor
3674 
Satisfactory
2653 

Length

Max length12
Median length9
Mean length6.9084474
Min length4

Characters and Unicode

Total characters147371
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoor
2nd rowPoor
3rd rowPoor
4th rowPoor
5th rowPoor

Common Values

ValueCountFrequency (%)
Good 5672
26.6%
Moderate 5033
23.6%
Very Poor 4029
18.9%
Poor 3674
17.2%
Satisfactory 2653
12.4%
Severe 271
 
1.3%
(Missing) 15
 
0.1%

Length

2023-06-20T05:19:46.742245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T05:19:47.064451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
poor 7703
30.4%
good 5672
22.4%
moderate 5033
19.8%
very 4029
15.9%
satisfactory 2653
 
10.5%
severe 271
 
1.1%

Most occurring characters

ValueCountFrequency (%)
o 34436
23.4%
r 19689
13.4%
e 14908
10.1%
d 10705
 
7.3%
a 10339
 
7.0%
t 10339
 
7.0%
P 7703
 
5.2%
y 6682
 
4.5%
G 5672
 
3.8%
M 5033
 
3.4%
Other values (8) 21865
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 117981
80.1%
Uppercase Letter 25361
 
17.2%
Space Separator 4029
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 34436
29.2%
r 19689
16.7%
e 14908
12.6%
d 10705
 
9.1%
a 10339
 
8.8%
t 10339
 
8.8%
y 6682
 
5.7%
i 2653
 
2.2%
s 2653
 
2.2%
f 2653
 
2.2%
Other values (2) 2924
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
P 7703
30.4%
G 5672
22.4%
M 5033
19.8%
V 4029
15.9%
S 2924
 
11.5%
Space Separator
ValueCountFrequency (%)
4029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 143342
97.3%
Common 4029
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 34436
24.0%
r 19689
13.7%
e 14908
10.4%
d 10705
 
7.5%
a 10339
 
7.2%
t 10339
 
7.2%
P 7703
 
5.4%
y 6682
 
4.7%
G 5672
 
4.0%
M 5033
 
3.5%
Other values (7) 17836
12.4%
Common
ValueCountFrequency (%)
4029
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 34436
23.4%
r 19689
13.4%
e 14908
10.1%
d 10705
 
7.3%
a 10339
 
7.0%
t 10339
 
7.0%
P 7703
 
5.2%
y 6682
 
4.5%
G 5672
 
3.8%
M 5033
 
3.4%
Other values (8) 21865
14.8%

Interactions

2023-06-20T05:19:19.596174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:16:56.557415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:02.019265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:09.512489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:15.310459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:21.885977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:28.667881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:34.448667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:41.975417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:48.002429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:59.919147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:07.398201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:13.769433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:19.400347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:26.628441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:32.865023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:39.994336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:46.462434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:53.380471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:00.185278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:06.444444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:13.140581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:19.990622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:16:56.797990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:02.272496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:09.776469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:15.552964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:22.308532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:28.917454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:34.838409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:42.240769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:48.394701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:00.369519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:07.815280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:14.016657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:19.700733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:26.873189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:33.111314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:40.253036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:46.724633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:53.752932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:00.443644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:06.830284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:13.372940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:20.393512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:16:57.053446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:02.528914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:10.031442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:15.806744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:22.729810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:29.167272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:35.242419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:42.501390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:48.676670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:00.875508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:08.242587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:14.276501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:20.075615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:27.730479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:33.349612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:40.501167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:46.992974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-20T05:19:12.646508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:19.071978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:26.538234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:01.772318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:09.275568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:15.073213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:21.481612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:28.429273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:34.155758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:41.743006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:47.754858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:17:59.483217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:07.086686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:13.533299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:19.163858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:26.396631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:32.625519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:39.747056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:46.231199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:52.978705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:18:59.954107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:06.064417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:12.907051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T05:19:19.318797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-20T05:19:47.340788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
COO3SO2PM25PM10NH3NOxPM10_24hr_avgPM25_24hr_avgSO2_24hr_avgNOx_24hr_avgNH3_24hr_avgCO_8hr_maxO3_8hr_maxPM25_SubIndexPM10_SubIndexSO2_SubIndexNOx_SubIndexNH3_SubIndexCO_SubIndexO3_SubIndexAQI_calculatedAQIclassChecksAQI_bucket_calculated
CO1.000-0.2690.8290.8950.8570.9180.9170.7370.7890.7670.8400.8170.8460.1560.7870.7360.7650.8380.8150.8460.1560.6980.3310.1200.245
O3-0.2691.0000.040-0.062-0.071-0.262-0.4640.1300.0980.096-0.0620.055-0.0180.6710.0980.1300.096-0.0620.055-0.0180.6710.2260.2480.0460.262
SO20.8290.0401.0000.8040.7770.7630.7720.6920.7130.7900.7450.7470.7440.3650.7120.6900.7880.7430.7450.7440.3650.6970.3030.0140.266
PM250.895-0.0620.8041.0000.9800.8290.7590.8790.8940.7980.8050.8260.8090.3140.8920.8770.7960.8030.8240.8090.3140.8340.4050.0800.370
PM100.857-0.0710.7770.9801.0000.8110.7420.8780.8590.7700.7470.7810.7500.3280.8570.8760.7680.7450.7790.7500.3280.8210.4330.0800.386
NH30.918-0.2620.7630.8290.8111.0000.8600.7190.7550.7470.7880.8370.7820.1620.7530.7170.7450.7860.8350.7820.1620.6600.2470.0600.194
NOx0.917-0.4640.7720.7590.7420.8601.0000.5680.6080.6270.7040.6460.665-0.0060.6060.5660.6250.7020.6450.665-0.0060.5090.2920.0940.218
PM10_24hr_avg0.7370.1300.6920.8790.8780.7190.5681.0000.9770.8550.8310.8620.7950.4400.9771.0000.8550.8310.8620.7950.4400.9270.4990.0910.710
PM25_24hr_avg0.7890.0980.7130.8940.8590.7550.6080.9771.0000.8720.8790.8950.8450.4051.0000.9770.8720.8790.8950.8450.4050.9230.4700.0980.697
SO2_24hr_avg0.7670.0960.7900.7980.7700.7470.6270.8550.8721.0000.8950.9020.8320.4140.8720.8551.0000.8950.9020.8320.4140.8340.4550.0990.496
NOx_24hr_avg0.840-0.0620.7450.8050.7470.7880.7040.8310.8790.8951.0000.9270.9150.2450.8790.8310.8951.0000.9270.9150.2450.7880.4100.2000.418
NH3_24hr_avg0.8170.0550.7470.8260.7810.8370.6460.8620.8950.9020.9271.0000.8910.3660.8950.8620.9020.9271.0000.8910.3660.8250.3660.1080.409
CO_8hr_max0.846-0.0180.7440.8090.7500.7820.6650.7950.8450.8320.9150.8911.0000.2580.8430.7930.8300.9130.8891.0000.2580.7600.3570.1380.355
O3_8hr_max0.1560.6710.3650.3140.3280.162-0.0060.4400.4050.4140.2450.3660.2581.0000.4050.4390.4140.2450.3660.2581.0000.5670.2960.0780.420
PM25_SubIndex0.7870.0980.7120.8920.8570.7530.6060.9771.0000.8720.8790.8950.8430.4051.0000.9780.8720.8790.8960.8430.4050.9230.5010.0730.738
PM10_SubIndex0.7360.1300.6900.8770.8760.7170.5661.0000.9770.8550.8310.8620.7930.4390.9781.0000.8560.8310.8620.7930.4390.9270.5170.0680.722
SO2_SubIndex0.7650.0960.7880.7960.7680.7450.6250.8550.8721.0000.8950.9020.8300.4140.8720.8561.0000.8950.9030.8300.4140.8340.4450.0780.493
NOx_SubIndex0.838-0.0620.7430.8030.7450.7860.7020.8310.8790.8951.0000.9270.9130.2450.8790.8310.8951.0000.9270.9130.2450.7880.4340.1390.434
NH3_SubIndex0.8150.0550.7450.8240.7790.8350.6450.8620.8950.9020.9271.0000.8890.3660.8960.8620.9030.9271.0000.8890.3660.8250.3730.0760.412
CO_SubIndex0.846-0.0180.7440.8090.7500.7820.6650.7950.8450.8320.9150.8911.0000.2580.8430.7930.8300.9130.8891.0000.2580.7600.4270.1340.400
O3_SubIndex0.1560.6710.3650.3140.3280.162-0.0060.4400.4050.4140.2450.3660.2581.0000.4050.4390.4140.2450.3660.2581.0000.5670.2940.0880.439
AQI_calculated0.6980.2260.6970.8340.8210.6600.5090.9270.9230.8340.7880.8250.7600.5670.9230.9270.8340.7880.8250.7600.5671.0000.4820.0800.903
AQIclass0.3310.2480.3030.4050.4330.2470.2920.4990.4700.4550.4100.3660.3570.2960.5010.5170.4450.4340.3730.4270.2940.4821.0000.0580.487
Checks0.1200.0460.0140.0800.0800.0600.0940.0910.0980.0990.2000.1080.1380.0780.0730.0680.0780.1390.0760.1340.0880.0800.0581.0000.076
AQI_bucket_calculated0.2450.2620.2660.3700.3860.1940.2180.7100.6970.4960.4180.4090.3550.4200.7380.7220.4930.4340.4120.4000.4390.9030.4870.0761.000

Missing values

2023-06-20T05:19:26.934752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-20T05:19:27.733948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-20T05:19:28.256383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AQIclassCOO3SO2PM25PM10NH3NOxPM10_24hr_avgPM25_24hr_avgSO2_24hr_avgNOx_24hr_avgNH3_24hr_avgCO_8hr_maxO3_8hr_maxPM25_SubIndexPM10_SubIndexSO2_SubIndexNOx_SubIndexNH3_SubIndexCO_SubIndexO3_SubIndexChecksAQI_calculatedAQI_bucket_calculated
Date
2021-01-01 00:00:0050.934606.089.1897.12112.4713.8126.25NaNNaNNaNNaNNaN0.934606.080.00.00.00.00.046.7306.0800002NaNNaN
2021-01-01 01:00:0051.094822.1712.16107.68124.9214.9533.54NaNNaNNaNNaNNaN1.094826.080.00.00.00.00.054.7416.0800002NaNNaN
2021-01-01 02:00:0051.522060.0617.41128.68150.5417.4852.20NaNNaNNaNNaNNaN1.522066.080.00.00.00.00.076.1036.0800002NaNNaN
2021-01-01 03:00:0051.815800.9620.98136.64159.9817.9970.56NaNNaNNaNNaNNaN1.815806.080.00.00.00.00.090.7906.0800002NaNNaN
2021-01-01 04:00:0051.3485012.1623.13107.91124.9016.4753.48NaNNaNNaNNaNNaN1.8158012.160.00.00.00.00.090.79012.1600002NaNNaN
2021-01-01 05:00:0050.9880136.1223.8492.03106.6515.3335.45NaNNaNNaNNaNNaN1.8158036.120.00.00.00.00.090.79036.1200002NaNNaN
2021-01-01 06:00:0050.7543665.8024.3282.8796.2713.8123.10NaNNaNNaNNaNNaN1.8158065.800.00.00.00.00.090.79065.8000002NaNNaN
2021-01-01 07:00:0050.4673094.4114.9063.6574.447.038.52NaNNaNNaNNaNNaN1.8158094.410.00.00.00.00.090.79094.4100002NaNNaN
2021-01-01 08:00:0050.42725100.1412.4057.1666.654.946.72NaNNaNNaNNaNNaN1.81580100.140.00.00.00.00.090.790100.2058822NaNNaN
2021-01-01 09:00:0050.4272598.7112.2854.9463.924.757.30NaNNaNNaNNaNNaN1.81580100.140.00.00.00.00.090.790100.2058822NaNNaN
AQIclassCOO3SO2PM25PM10NH3NOxPM10_24hr_avgPM25_24hr_avgSO2_24hr_avgNOx_24hr_avgNH3_24hr_avgCO_8hr_maxO3_8hr_maxPM25_SubIndexPM10_SubIndexSO2_SubIndexNOx_SubIndexNH3_SubIndexCO_SubIndexO3_SubIndexChecksAQI_calculatedAQI_bucket_calculated
Date
2023-06-20 01:00:0010.2636920.744.176.1810.241.935.0710.9283336.8829173.9329174.3054171.3812500.2803822.1711.47152810.9283334.9161465.3817710.34531314.01922.17722.0Good
2023-06-20 02:00:0010.2870619.494.597.0212.812.286.9410.9183336.8312503.9479174.3358331.3925000.2870622.1711.38541710.9183334.9348965.4197920.34812514.35322.17722.0Good
2023-06-20 03:00:0010.2803821.994.356.7313.752.096.5411.0191676.8191673.9700004.3712501.4012500.2870622.1711.36527811.0191674.9625005.4640620.35031314.35322.17722.0Good
2023-06-20 04:00:0010.2503429.333.074.289.291.313.6211.1254176.8212503.9925004.3975001.4062500.2870629.3311.36875011.1254174.9906255.4968750.35156214.35329.33729.0Good
2023-06-20 05:00:0010.2403334.332.743.677.441.172.5711.2016676.8233334.0133334.4179171.4095830.2870634.3311.37222211.2016675.0166675.5223960.35239614.35334.33734.0Good
2023-06-20 06:00:0010.2336538.982.444.137.141.061.9911.2558336.8308334.0279174.4316671.4116670.2870638.9811.38472211.2558335.0348965.5395830.35291714.35338.98739.0Good
2023-06-20 07:00:0010.2269744.352.034.666.940.951.6311.3154176.8720834.0316674.4362501.4133330.2870644.3511.45347211.3154175.0395835.5453120.35333314.35344.35744.0Good
2023-06-20 08:00:0010.2236446.491.975.617.780.931.6411.4075006.9587504.0279174.4370831.4150000.2870646.4911.59791711.4075005.0348965.5463540.35375014.35346.49746.0Good
2023-06-20 09:00:0010.2236447.212.065.358.160.901.7411.4862507.0137504.0179174.4362501.4179170.2870647.2111.68958311.4862505.0223965.5453120.35447914.35347.21747.0Good
2023-06-20 10:00:0010.2269746.491.974.898.880.871.7611.5529177.0129173.9995834.4337501.4220830.2803847.2111.68819411.5529174.9994795.5421870.35552114.01947.21747.0Good